Indirect Training Algorithms for Spiking Neural Networks Controlled Virtual Insect Navigation

Abstract

Even though Articial Neural Networks have been shown capable of solving many problems such as pattern recognition, classication, function approximation, clinics, robotics, they suers intrinsic limitations, mainly for processing large amounts of data or for fast adaptation to a changing environment. Several characteristics, such as iterative learning algorithms or articially designed neuron model and network architecture, are strongly restrictive compared with biological processing in natural neural networks. Spiking neural networks as the newest generation of neural models can overcome the weaknesses of ANNs. Because of the biologically realistic properties, the electrophysiological recordings of neural circuits can be compared to the outputs of the corresponding spiking neural network simulated on the computer, determining the plausibility of the starting hypothesis. Comparing with ANN, it is known that any function that can be computed by a sigmoidal neural network can also be computed by a small network of spiking neurons. In addition, for processing a large amount of data, SNNs can transmit and receive a large amount of data through the timing of the spikes and remarkably decrease the interactions load between neurons. This makes possible for very ecient parallel implementations.Many training algorithms have been proposed for SNN training mainly based on the direct update of the synaptic plasticities or weights. However, the weights can not be changed directly and, instead, can be changed by the interactions of pre- and postsynaptic neural activities in many potential applications of adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale neuromorphic chips. The eciency of the bio-inspired, neuromorphic processing exposes the shortcomings of digital computing. After trained, the simulated neuromorphic model can be applied to speaker recognition, looming detection and temporal pattern matching. The properties of the neuromorphic chip enable it to solve the same problem while using fewer energies comparing with other hardware. The neuromorphic chips need applicable training methods that do not require direct manipulations of the connection strength.Nowadays, thanks to fast improvements in hardware for neural stimulation and recording technologies, neurons in vivo and vitro can be controlled to re precisely in milliseconds. These improvements enable the study on the link between synaptic level and functional-level plasticity in the brain. However, existing training methods rely on learning rules for manipulating synaptic weights and on detailed knowledge of the network connectivity and synaptic strengths. New training algorithms that do not require the knowledge of the synaptic weights or connections are needed while they cannot require direct manipulations of the synaptic strength.This thesis presents indirect training methods to train spiking neural networks,which can both modeling neuromorphic chips and biological neural networks in vivo, via extra stimulus without the knowledge of synaptic strengths and connections. The algorithms are based on the spike timing-dependent plasticity rule by controlling input spike trains. One of the algorithms minimizes the error between the synaptic weight and the optimal weight, by stimulating the input neuron with an adaptive pulse training determined by the gradient of the error function. Another algorithm uses numerical gradient of the output error with respect to the weight change to control the training stimulus, which are injected to the neural network for controlling a virtual insect for navigating and nding target in an unknown terrain. Finally, the newest algorithm uses indirect perturbation of the temporal dierences between the extra stimulus in order to train a large spiking neural network. The trained spiking neural network can control both a unicycle modeled virtual insect and a virtual insectmoving in a tripod gait. The results show that these indirect training algorithms can train SNNs for solving control problems. In the thesis, the trained insect can and its target while avoiding obstacles in an unknown terrain. Future studies will focus on improving the insect's movement to using more complex locomotion model. The training algorithms will also be applied to biological neural networks and CMOS memristors. The trained neural networks will also be used for controlling flying microrobots.